IJRET: International Journal of Research in Engineering and Technology
eISSN: 2319-1163 | pISSN: 2321-7308
PERFORMANCE COMPARISON OF BLIND ADAPTIVE MULTIUSER DETECTION ALGORITHMS Khalifa Hunki1, EhabM. Shaheen2, Mohamed Soliman3, K. A. El-Barbary4 1, 2, 3
Military Technical College, Cairo, Egypt 4 Suez Canal University, Suez, Egypt hunki07@gmai.com, efareed2007@yahoo.com, mnna3000@yahoo.com, khbar2000@yahoo.com
Abstract Blind multiuser detection algorithms are used to eliminate the Multiple Access Interference (MAI) and the Near-Far effect in mobile communication systems. Four kinds of blind multiuser detection algorithms applied to code-division multiple-access (CDMA) communication system are studied in this paper. Those algorithms are the Least Mean Squares (LMS), Recursive Least Squares (RLS), Kalman filter and subspace-based Kalman filter algorithms. The resultant signal to interference ratio (SIR) at the output of the receivers controlled by the four kinds of multi-user detection algorithm has been discussed in this paper. Simulation results show that the subspace based Kalman filter algorithm outperforms all other three algorithms. Subspace-based Kalman filter algorithm has faster convergence speed, more practical and the capability of CDMA system can be increased.
Keywords: Blind multiuser detection, Code-division multiple access, Mean output energy, Adaptive filtering. ---------------------------------------------------------------------***---------------------------------------------------------------------1. INTRODUCTION Direct-sequence code-division multiple-access (DS-CDMA) has been widely studied in the literatures. Recently, adaptive interference suppression techniques based on multiuser detection have been considered as powerful methods for increasing the quality, capacity, and coverage of CDMA systems [1]. The mitigation of MAI in CDMA systems is a problem of continuing interest since MAI is the dominant impairment for CDMA systems. It is widely recognized that MAI exists even in perfect power-controlled CDMA systems [2]. Multiuser detectors perform better than the conventional detector under all power distributions, except in pathological cases, such as the decorrelating detector in extremely low signal to noise ratio (SNR) [2]. Therefore, multiuser detection is not only a solution to the near-far problem but is also useful even with power control. In order to successfully eliminate the MAI and detect the desired user‟s information bits, one or more of the following is usually required at the receiver end: 1) Spreading waveform of the desired user; 2) Spreading waveforms of the interfering users; 3) Propagation delay (timing) of the desired user; 4) Propagation delays of the interfering users; 5) Received amplitudes of the interfering users (relative to that of the desired user); 6) Training data sequences for every active user
The “blind” adaptive multiuser detectors require only the knowledge of (1) and (3), which is, the same knowledge as the conventional receiver. Previous work on blind adaptive multiuser detection dates back to a 1995 paper by Honig et al. [3], who established a canonical representation for blind multiuser detectors and used stochastic gradient algorithms such as LMS to implement the blind adaptive mean output energy (MOE) detector. As elegantly shown by Roy [4], the blind adaptive MOE detector has a smaller eigenvalue spread than the training-based adaptive LMS detector; hence, the blind LMS algorithm always provides (nominally) faster convergence than the training driven LMS-MMSE receiver but at the cost of increased tap-weight fluctuation or misadjustment. It is well-known that the RLS algorithm and the Kalman filtering algorithm are better than the LMS algorithm in convergence rate and tracking capability [2]. Using the exponentially weighted sum of error squares cost function, Chen and Roy [5] proposed an RLS algorithm that requires the knowledge of (1)–(4) and, thus, is not a blind multiuser detector. Later, Poor and Wang [6] proposed an exponentially windowed RLS algorithm for blind multiuser detection requiring only the knowledge of (1) and (3). On the other hand, the RLS is a special case of the Kalman filter [7], [8], whereas the Kalman filter is known to be a linear minimum variance state estimator [7], [9] and [10]. Motivated probably by these two facts, some attention has been focused on Kalman filter-based adaptive multiuser
__________________________________________________________________________________________ Volume: 02 Issue: 11 | Nov-2013, Available @ http://www.ijret.org
454